This paper studies a model learning and online planning approach towards building flexible and general robots. Specifically, we investigate how to exploit the locality and sparsity structures in the underlying environmental transition model to improve model generalization, data-efficiency, and runtime-efficiency. We present a new domain definition language, named PDSketch. It allows users to flexibly define high-level structures in the transition models, such as object and feature dependencies, in a way similar to how programmers use TensorFlow or PyTorch to specify kernel sizes and hidden dimensions of a convolutional neural network. The details of the transition model will be filled in by trainable neural networks. Based on the defined structures and learned parameters, PDSketch automatically generates domain-independent planning heuristics without additional training. The derived heuristics accelerate the performance-time planning for novel goals.

翻译：本文研究一种结合模型学习与在线规划的通用机器人构建方法。具体而言，我们探讨如何利用底层环境转移模型中的局部性和稀疏性结构，以提升模型泛化能力、数据效率及运行时效率。为此，我们提出一种新的领域定义语言PDSketch。该语言允许用户灵活定义转移模型中的高层结构（如对象与特征依赖关系），其方式类似于程序员使用TensorFlow或PyTorch指定卷积神经网络的卷积核尺寸与隐藏维度。转移模型的细节部分可通过可训练神经网络自动填充。基于已定义的结构与学习参数，PDSketch无需额外训练即可自动生成领域无关的规划启发式方法。由此导出的启发式方法可加速面向新目标的性能规划过程。